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In a previous blog, we discussed how most charterers monitor performance by using data is collected manually at sea and reported back to shore once per day. Also, this noon data is used in performance claims.  We believe that the smart use of high-quality data can also help you in the settlement of these claims.

In a recent project, We4Sea was approached by a charterer that suspected underperformance of one of their chartered vessels, during a laden leg from Brazil to China. During a stop in Singapore, an intermediate hull cleaning was carried out. The vessel experienced time loss which was blamed on bad weather by the owners.

The charterer suspected that not only bad weather contributed to the time loss, but also poor maintenance and hull fouling.  The standard way of calculating Charter Party compliance was not possible, as the whole voyage was considered bad weather.

We4Sea investigated the magnitude of speed loss due to bad weather, poor maintenance, and hull fouling by comparing the data reported by the ship to a digital modeled version of the ship, the Digital Twin.

The voyage has been ‘re-sailed’ using the Digital Twin in various configurations, yielding both the extra fuel consumption due to hull fouling, as well as the speed the vessel would have been able to sail were she in good condition.

Also, it was found that there was no overconsumption after the hull cleaning, proving the vessel was severely fouled on the first leg.

Based on 11,000 data points for the whole voyage, we were able to make a substantiated overconsumption claim of over 200 MT, and a 1,5 days time loss for the entire voyage. A substantial settlement was reached based on this analysis.

The conventional approach, with only 40 data points (being the noon-reports) was just not good enough. Smart data did the trick.

Interested to learn how data can help you?  Please contact us for a joint pilot!



Schieweg 93
2627 AT Delft
The Netherlands

EU H2020
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 877766.
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